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Data Scientist at Foghorn Systems

2 positions are available in each location. Title seniority and salary are set to experience level of the applicant.Role and ResponsibilitiesExecute data mining projects, training and deploying models over a typical duration of 2 -12 months.The ideal candidate should be able to innovate, analyze the customer requirement, develop a solution in the time box of the project plan, execute and deploy the solution.Integrate the data mining projects embedded data mining applications in the FogHorn platform (on Docker or Android).Core QualificationsCandidates must meet ALL of the following qualifications:Have analyzed, trained and deployed at least three data mining models in the past. If the candidate did not directly deploy their own models, they will have worked with others who have put their models into production. The models should have been validated as robust over at least an initial time period.Three years of industry work experience, developing data mining models which were deployed and used.Programming experience in Python is core using data mining related libraries like Scikit-Learn. Other relevant Python mining libraries include NumPy, SciPy and Pandas.Data mining algorithm experience in at least 3 algorithms across: prediction (statistical regression, neural nets, deep learning, decision trees, SVM, ensembles), clustering (k-means, DBSCAN or other) or Bayesian networksBonus QualificationsAny of the following extra qualifications will make a candidate more competitive:Soft SkillsSets expectations, develops project plans and meets expectations.Experience adapting technical dialogue to the right level for the audience (i.e. executives) or specific jargon for a given vertical market and job function.Technical skillsCommonly, candidates have a MS or Ph.D. in Computer Science, Math, Statistics or an engineering technical discipline. BS candidates with experience are considered.Have managed past models in production over their full life cycle until model replacement is needed. Have developed automated model refreshing on newer data. Have developed frameworks for model automation as a prototype for product.Training or experience in Deep Learning, such as TensorFlow, Keras, convolutional neural networks (CNN) or Long Short Term Memory (LSTM) neural network architectures. If you dont have deep learning experience, we will train you on the job.Shrinking deep learning models, optimizing to speed up execution time of scoring or inference.OpenCV or other image processing tools or librariesCloud computing: Google Cloud, Amazon AWS or Microsoft Azure. We have integration with Google Cloud and are working on other integrations.Decision trees like XGBoost or Random Forests is helpful.Complex Event Processing (CEP) or other streaming data as a data source for data mining analysisTime series algorithms from ARIMA to LSTM to Digital Signal Processing (DSP).Bayesian Networks (BN), a.k.a. Bayesian Belief Networks (BBN) or Graphical Belief Networks (GBN)Experience with PMML is of interest (see experience in Industrial Internet of Things (IoT) applications:Energy: Oil and Gas, Wind TurbinesManufacturing: Motors, chemical processes, tools, automotiveSmart Cities: Elevators, cameras on population or cars, power gridTransportation: Cars, truck fleets, trainsHow To ApplyTo apply, submit a resume to, with an email subject: Greg (Data Scientist) .If you want to be more competitive, address how you meet all the minimum requirements and any bonus qualifications.About FogHorn SystemsFogHorn is a leading developer of edge intelligence software for industrial and commercial IoT application solutions. FogHorns Lightning software platform brings the power of advanced analytics and machine learning to the on-premise edge environment enabling a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance and operational intelligence use cases. FogHorns technology is ideally suited for OEMs, systems integrators and end customers in manufacturing, power and water, oil and gas, renewable energy, mining, transportation, healthcare, retail, as well as Smart Grid, Smart City, Smart Building and connected vehicle applications.Press: Edge Computing Company of the Year Compass Intelligence2019 Internet of Things 50: 10 Coolest Industrial IoT Companies CRN2018 IoT Planforms Leadership Award & Edge Computing Excellence IoT Evolution World Magazine2018 10 Hot IoT Startups to Watch Network World. (Gartner estimated 20 billion connected things in use worldwide by 2020)2018 Winner in Artificial Intelligence and Machine Learning Globe Awards2018 Ten Edge Computing Vendors to Watch ZDNet & 451 Research2018 The 10 Most Innovative AI Solution Providers Insights Success2018 The AI 100 CB Insights2017 Cool Vendor in IoT Edge Computing Gartner2017 20 Most Promising AI Service Providers CIO ReviewOur Series A round was for $15 million. Our Series B round was for $30 million October 2017. Investors include: Saudi Aramco Energy Ventures, Intel Capital, GE, Dell, Bosch, Honeywell and The Hive.About the Data Science Solutions teamIn 2018, our Data Science Solutions team grew from 4 to 9. We are growing again from 11. We work on revenue generating projects for clients, such as predictive maintenance, time to failure, manufacturing defects. About half of our projects have been related to vision recognition or deep learning. We are not only working on consulting projects but developing vertical solution applications that run on our Lightning platform, with embedded data mining.Our data scientists like our team because:We care about best practicesHave a direct impact on the companys revenueGive or receive mentoring as part of the collaborative processQuestions and challenging the status quo with data is safeIntellectual curiosity balanced with humilityPresent papers or projects in our Thought Leadership meeting series, to support continuous learning

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